Satisficing is a central concept in both individual and social multiagent decision making. In this paper we first extend the notion of satisficing by formally modeling the tradeoff between costs and decision failure. Second, we extend this notion of “neo”-satisficing into the context of social or multiagent decision making and interaction, and model the social conditioning of preferences in a satisficing framework.

ABSTRACTHigh-level judgement and decision-making tasks display dynamic bidirectional relationships in which salient cues determine how responses are evaluated by decision-makers, and these responses in turn determine the cues that are considered. In this paper, we propose Kosko's bidirectional associative memory network, a minimal two-layer recurrent neural network, as a mathematically tractable toy model with which the properties of existing bidirectional models, and the behavioural implications of these properties, can be studied. We first derive results regarding the dynamics of the BAM (...) network, and then show how these results can be used to provide an analytically sound explanation for a number of important findings, including coherence shifts in judgement and choice, anchoring effects, and reference point effects. (shrink)